<p>Deploying deep learning models in resource-constrained environments requires training strategies that improve compact models without increasing inference-time cost. This work addresses this challenge through an efficient self-distillation framework. We propose Layered Self-Supervised Knowledge Distillation (LSSKD), a training framework that enhances compact deep neural networks without relying on large pre-trained teacher models. The method introduces auxiliary classifiers at intermediate network stages to generate self-supervised knowledge and enable hierarchical knowledge transfer across layers. Experiments on CIFAR-100, Tiny-ImageNet, and ImageNet demonstrate that LSSKD consistently improves the performance of compact models. The method achieves an average improvement of 4.54% over PS-KD and a 1.14% gain over SSKD on CIFAR-100, while improving performance on ImageNet by 0.32% compared with HASSKD. Additional evaluations under few-shot settings further demonstrate the effectiveness of the Proposed LSSKD framework. These findings demonstrate that the Proposed LSSKD framework effectively improves the generalization and performance of compact models without relying on large over-parameterized teacher networks. Importantly, auxiliary classifiers are used only during training and can be removed at inference time, introducing no additional computational cost. This design makes the approach well suited for improving compact vision models intended for resource-constrained deployment scenarios.</p>